class_docstr: This is a class that allows one to apply any given (fixed) 
  transformation to the output from the loader in *real-time*.
  
  For instance, you could use for applications such as custom 
  data augmentation and adding image/label noise.
  
  Note that the LambdaLoader is the final transformation that
  is applied to image-label pairs from the dataset as part of the
  loading process---i.e., other (standard) transformations such
  as data augmentation can only be applied *before* passing the
  data through the LambdaLoader.
  
  For more information see :ref:`our detailed walkthrough <using-custom-loaders>`
code: def __getattr__(self, attr):
      return getattr(self.data_loader, attr)
  
imports: import argparse
  from .None import cifar_models
  from .tools import folder
  import os
  import shutil
  import time
  import numpy
  import torch
  import torch.utils.data
  from torch.utils.data import DataLoader
  from torch.utils.data import Subset
  import torchvision.transforms
  from torch.utils.data import DataLoader
  from .None import imagenet_models
label_desc: Use image node_id 6 for calling the LambdaLoader obj's underlying member method code with example usage: obj.__getattr__(attr) and returns: getattr